301,681 research outputs found
Higher-order neural network software for distortion invariant object recognition
The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing
Pattern Matching and Neural Networks based Hybrid Forecasting System
Copyright © 2001 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Advances in Pattern Recognition — ICAPR 2001Second International Conference on Advances in Pattern Recognition (ICAPR 2001), Rio de Janeiro, Brazil, March 11–14, 2001In this paper we propose a Neural Net-PMRS hybrid for forecasting time-series data. The neural network model uses the traditional MLP architecture and backpropagation method of training. Rather than using the last n lags for prediction, the input to the network is determined by the output of the PMRS (Pattern Modelling and Recognition System). PMRS matches current patterns in the time-series with historic data and generates input for the neural network that consists of both current and historic information. The results of the hybrid model are compared with those of neural networks and PMRS on their own. In general, there is no outright winner on all performance measures, however, the hybrid model is a better choice for certain types of data, or on certain error measures
Using Connectionist Models to Evaluate Examinees’ Response Patterns to Achievement Tests
The attribute hierarchy method (AHM) applied to assessment engineering is described. It is a psychometric method for classifying examinees’ test item responses into a set of attribute mastery patterns associated with different components in a cognitive model of task performance. Attribute probabilities, computed using a neural network, can be estimated for each examinee thereby providing specific information about the examinee’s attribute-mastery level. The pattern recognition approach described in this study relies on an explicit cognitive model to produce the expected response patterns. The expected response patterns serve as the input to the neural network. The model also yields the cognitive test specifications. These specifications identify the examinees’ attribute patterns which are used as output for the neural network. The purpose of the statistical pattern recognition analysis is to estimate the probability that an examinee possess specific attribute combinations based on their observed item response patterns. Two examples using student response data from a sample of algebra items on the SAT illustrate our pattern recognition approach
A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing
The current study uses a novel method of multilevel neurons and high order
synchronization effects described by a family of special metrics, for pattern
recognition in an oscillatory neural network (ONN). The output oscillator
(neuron) of the network has multilevel variations in its synchronization value
with the reference oscillator, and allows classification of an input pattern
into a set of classes. The ONN model is implemented on thermally-coupled
vanadium dioxide oscillators. The ONN is trained by the simulated annealing
algorithm for selection of the network parameters. The results demonstrate that
ONN is capable of classifying 512 visual patterns (as a cell array 3 * 3,
distributed by symmetry into 102 classes) into a set of classes with a maximum
number of elements up to fourteen. The classification capability of the network
depends on the interior noise level and synchronization effectiveness
parameter. The model allows for designing multilevel output cascades of neural
networks with high net data throughput. The presented method can be applied in
ONNs with various coupling mechanisms and oscillator topology.Comment: 26 pages, 24 figure
VLSI Implementation of Modified Hamming Neural Network for non Binary Pattern Recognition
Artificial intelligence is integral part of a neural network is based on mathematical equations and artificial neurons. The focus here is the implementation of the Artificial Neural Network Architecture (ANN) with on chip learning in analog VLSI for pattern recognition. It is a maximum likelohood classifier which can be implemented using VLSI. Modified Hamming neural network architecture is presented.Thenew circuit is modified to accept real time inputs as well as to determine next close pattern with respect to input pattern.Modified digit recognition circuit was simulated using HSPICE level 49 model parameters with version 3.1180n at VDD of 3V. The circuit shows power consumption of 34mW and transient delay of 0.35nS
Deep HMResNet Model for Human Activity-Aware Robotic Systems
Endowing the robotic systems with cognitive capabilities for recognizing
daily activities of humans is an important challenge, which requires
sophisticated and novel approaches. Most of the proposed approaches explore
pattern recognition techniques which are generally based on hand-crafted
features or learned features. In this paper, a novel Hierarchal Multichannel
Deep Residual Network (HMResNet) model is proposed for robotic systems to
recognize daily human activities in the ambient environments. The introduced
model is comprised of multilevel fusion layers. The proposed Multichannel 1D
Deep Residual Network model is, at the features level, combined with a
Bottleneck MLP neural network to automatically extract robust features
regardless of the hardware configuration and, at the decision level, is fully
connected with an MLP neural network to recognize daily human activities.
Empirical experiments on real-world datasets and an online demonstration are
used for validating the proposed model. Results demonstrated that the proposed
model outperforms the baseline models in daily human activity recognition.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606
KOMBINASI ALGORITMA JARINGAN SYARAF TIRUAN LEARNING VECTOR QUANTIZATION (LVQ) DAN SELF ORGANIZING KOHONEN PADA KECEPATAN PENGENALAN POLA TANDA TANGAN
Signature is a special form of handwriting that contain special characters and additional forms are often used as proof of a person's identity verification. Partially legible signature, but many signatures that can not be read. However, a signature can be handled as an image so that it can be recognized using pattern recognition applications in image processing. Because the signature is the primary mechanism for authentication and authorization in legal transactions,the need for research on the development of recognition applications and automatic signature verification and efficiently increases from year to year. The method is widely used in signature recognition is a method of artificial neural network. On artificial neural networks are learning and recognition. One neural network algorithm is Learning Vector Quantization ( LVQ ) and Self Organizing Kohonen. Processes that occur in the neural network method requires a relatively long time. It is influenced by the number of data samples are used as a means of weight training update. The more and the large size of the pattern being trained, the longer the time it takes the network. LVQ is a method of training the unsupervised competitive layer will automatically learn to classify input vectors into certain classes. The classes are generated depends on the distance between the input vectors. If there are 2 input vectors are nearly as competitive layer will then classify both the input vectors into the same class. Kohonen Self Organizing Network is one of the neural network model which uses learning methods or unguided unsupervised neural network model that resembles humans. To speed up the computing process in the training and recognition is then developed an algorithm and a combination of LVQ and Self Organizing Kohonen by modifying the weight given to obtain a shorter time in the process of training and recognition
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